Source: US Environmental Protection Agency EJSCREEN Tool, 2020 data (last modified 7/1/21)
EJSCREEN is an “environmental justice (EJ) mapping and screening tool” produced by the EPA.
glimpse(ejscreen)
## Rows: 155
## Columns: 38
## $ ID <chr> "510030101001", "510030101002", "510030101003", "5100301020…
## $ statefips <dbl> 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51,…
## $ countyfips <chr> "003", "003", "003", "003", "003", "003", "003", "003", "00…
## $ tractfips <chr> "010100", "010100", "010100", "010201", "010201", "010202",…
## $ blkgpfips <dbl> 1, 2, 3, 1, 2, 1, 2, 1, 2, 3, 4, 5, 1, 2, 3, 1, 2, 1, 2, 3,…
## $ PRE1960PCT <dbl> 0.071991001, 0.298299845, 0.256756757, 0.027385892, 0.08238…
## $ DSLPM <dbl> 0.1316935, 0.1316935, 0.1316935, 0.1974282, 0.1974282, 0.21…
## $ CANCER <dbl> 24.95765, 24.95765, 24.95765, 28.33822, 28.33822, 28.15892,…
## $ RESP <dbl> 0.3120049, 0.3120049, 0.3120049, 0.3656255, 0.3656255, 0.35…
## $ PTRAF <dbl> NA, NA, 0.31287689, 286.71433099, 4.07514122, 2.43054152, 9…
## $ PWDIS <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.28153…
## $ PNPL <dbl> 0.03555328, 0.05853532, 0.06053500, 0.02780625, 0.03162721,…
## $ PRMP <dbl> 0.05435428, 0.09491017, 0.14614959, 0.05496790, 0.04548612,…
## $ PTSDF <dbl> 0.10236159, 0.06039390, 0.08200253, 0.21222923, 0.23416369,…
## $ OZONE <dbl> 41.64513, 41.64513, 41.64513, 41.65850, 41.65850, 41.65348,…
## $ PM25 <dbl> 7.241029, 7.241029, 7.241029, 7.386133, 7.386133, 7.364257,…
## $ P_LDPNT <dbl> 33.48404, 62.76819, 58.92123, 21.17666, 35.58471, 51.33492,…
## $ P_DSLPM <dbl> 7.637395, 7.637395, 7.637395, 18.744228, 18.744228, 21.5940…
## $ P_CANCR <dbl> 22.04262, 22.04262, 22.04262, 35.76293, 35.76293, 34.95291,…
## $ P_RESP <dbl> 19.50741, 19.50741, 19.50741, 32.45459, 32.45459, 30.91068,…
## $ P_PTRAF <dbl> NA, NA, 5.341820, 56.287080, 8.495607, 7.452488, 11.507111,…
## $ P_PWDIS <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 49.2460…
## $ P_PNPL <dbl> 31.93691, 48.19756, 49.36919, 25.50034, 28.75482, 36.94343,…
## $ P_PRMP <dbl> 5.555356, 14.571876, 26.639997, 5.670226, 3.932652, 8.65649…
## $ P_PTSDF <dbl> 14.103431, 7.377108, 10.976983, 26.610963, 28.217063, 21.23…
## $ P_OZONE <dbl> 40.64001, 40.64001, 40.64001, 40.75917, 40.75917, 40.71748,…
## $ P_PM25 <dbl> 16.10605, 16.10605, 16.10605, 18.16651, 18.16651, 17.83501,…
## $ T_LDPNT <chr> "0.072 = fraction pre-1960 (33%ile)", "0.3 = fraction pre-1…
## $ T_DSLPM <chr> "0.132 ug/m3 (7%ile)", "0.132 ug/m3 (7%ile)", "0.132 ug/m3 …
## $ T_CANCR <chr> "25 lifetime risk per million (22%ile)", "25 lifetime risk …
## $ T_RESP <chr> "0.31 (19%ile)", "0.31 (19%ile)", "0.31 (19%ile)", "0.37…
## $ T_PTRAF <chr> NA, NA, "0.31 daily vehicles/meters distance (5%ile)", "290…
## $ T_PWDIS <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.0000…
## $ T_PNPL <chr> "0.036 sites/km distance (31%ile)", "0.059 sites/km distanc…
## $ T_PRMP <chr> "0.054 facilities/km distance (5%ile)", "0.095 facilities/k…
## $ T_PTSDF <chr> "0.1 facilities/km distance (14%ile)", "0.06 facilities/km …
## $ T_OZONE <chr> "41.6 ppb (40%ile)", "41.6 ppb (40%ile)", "41.6 ppb (40%ile…
## $ T_PM25 <chr> "7.24 ug/m3 (16%ile)", "7.24 ug/m3 (16%ile)", "7.24 ug/m3 (…
Observations are block group estimates of key environmental indicators:
PRE1960PCT)DSLPM and PM25)CANCER)RESP)PTRAF)PNPL)PRMP)PTSDF)OZONE)PWDIS)P_ indicates percentile ranks for each variable, and T_ indicates map popup text.
ejscreen %>% select(-c(ID:blkgpfips, T_LDPNT:T_PM25)) %>%
select(where(~is.numeric(.x) && !is.na(.x))) %>%
as.data.frame() %>%
stargazer(., type = "text", title = "Summary Statistics", digits = 0,
summary.stat = c("mean", "sd", "min", "median", "max"))
##
## Summary Statistics
## =======================================
## Statistic Mean St. Dev. Min Median Max
## ---------------------------------------
## PRE1960PCT 0 0 0 0 1
## DSLPM 0 0 0 0 1
## CANCER 30 3 23 29 34
## RESP 0 0 0 0 0
## PNPL 0 0 0 0 0
## PRMP 0 0 0 0 1
## PTSDF 1 1 0 0 3
## OZONE 41 0 41 41 42
## PM25 7 0 7 7 8
## P_LDPNT 47 22 11 48 98
## P_DSLPM 34 21 6 25 73
## P_CANCR 43 13 16 40 62
## P_RESP 39 13 13 37 60
## P_PNPL 35 14 16 33 95
## P_PRMP 13 14 2 7 79
## P_PTSDF 34 21 4 32 74
## P_OZONE 38 3 33 39 43
## P_PM25 19 2 13 19 23
## ---------------------------------------
ejscreen %>% select(ID, PRE1960PCT:PM25) %>%
pivot_longer(-ID, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
geom_histogram() +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% c("PRE1960PCT", "DSLPM", "CANCER", "RESP", "PTRAF", "PWDIS", "PNPL", "PRMP", "PTSDF", "OZONE", "PM25")) %>%
mutate(label = paste0(varname, ": ", description)) %>%
select(label) %>%
as.list()
## $label
## [1] "PRE1960PCT: % of housing built before 1960 -- lead paint indicator"
## [2] "DSLPM: Diesel particulate matter level in the air, measured in micrograms per cubic meter"
## [3] "CANCER: Cancer risk due to toxics in the air"
## [4] "RESP: \"Ratio of exposure concentration to health-based reference concentration\""
## [5] "PTRAF: Average number of daily vehicles at major roads divided by distance in meters"
## [6] "PWDIS: Toxicity-weighted stream concentrations divided by distance in kilometers"
## [7] "PNPL: Number of National Priorities List (NPL) sites within 5 km divided by distance in kilometers"
## [8] "PRMP: Number of Risk Management Plan (RMP) facilities within 5 km divided by distance in kilometers"
## [9] "PTSDF: Number of Treatment Storage and Disposal (TSDF) facilities within 5 km divided by distance in kilometers"
## [10] "OZONE: Summer daily average of ozone concentration in the air, in parts per billion"
## [11] "PM25: Yearly average PM2.5 level in the air, measured in micrograms per cubic meter"
The following figure shows the correlations among primary measures. The darker the color, the more highly correlated a pair of variables are.
ejscreen %>% select(PRE1960PCT:PM25) %>%
ggcorr(label = TRUE, label_alpha = TRUE)
meta %>%
filter(varname %in% c("PRE1960PCT", "DSLPM", "CANCER", "RESP", "PTRAF", "PWDIS", "PNPL", "PRMP", "PTSDF", "OZONE", "PM25")) %>%
mutate(label = paste0(varname, ": ", description)) %>%
select(label) %>%
as.list()
## $label
## [1] "PRE1960PCT: % of housing built before 1960 -- lead paint indicator"
## [2] "DSLPM: Diesel particulate matter level in the air, measured in micrograms per cubic meter"
## [3] "CANCER: Cancer risk due to toxics in the air"
## [4] "RESP: \"Ratio of exposure concentration to health-based reference concentration\""
## [5] "PTRAF: Average number of daily vehicles at major roads divided by distance in meters"
## [6] "PWDIS: Toxicity-weighted stream concentrations divided by distance in kilometers"
## [7] "PNPL: Number of National Priorities List (NPL) sites within 5 km divided by distance in kilometers"
## [8] "PRMP: Number of Risk Management Plan (RMP) facilities within 5 km divided by distance in kilometers"
## [9] "PTSDF: Number of Treatment Storage and Disposal (TSDF) facilities within 5 km divided by distance in kilometers"
## [10] "OZONE: Summer daily average of ozone concentration in the air, in parts per billion"
## [11] "PM25: Yearly average PM2.5 level in the air, measured in micrograms per cubic meter"
pal <- colorNumeric("plasma", reverse = TRUE, domain = cvilleshapes$PTSDF)
leaflet(cvilleshapes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvilleshapes,
fillColor = ~pal(PTSDF),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", cvilleshapes$GEOID, "<br>",
"Proximity to TSDF: ", cvilleshapes$T_PTSDF)) %>%
addLegend("bottomright", pal = pal, values = cvilleshapes$PTSDF,
title = "Proximity to TSDF", opacity = 0.7)
pal <- colorNumeric("plasma", reverse = TRUE, domain = cvilleshapes$PTRAF)
leaflet(cvilleshapes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvilleshapes,
fillColor = ~pal(PTRAF),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", cvilleshapes$GEOID, "<br>",
"Proximity to traffic: ", cvilleshapes$T_PTRAF)) %>%
addLegend("bottomright", pal = pal, values = cvilleshapes$PTRAF,
title = "Traffic Proximity", opacity = 0.7)
pal <- colorNumeric("Blues", reverse = TRUE, domain = cvilleshapestr$PM25)
leaflet(cvilleshapestr) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvilleshapestr,
fillColor = ~pal(PM25),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", cvilleshapestr$GEOID, "<br>",
"PM2.5 Level: ", cvilleshapestr$T_PM25)) %>%
addLegend("bottomright", pal = pal, values = cvilleshapestr$PM25,
title = "PM2.5 Concentrations", opacity = 0.7)
pal <- colorNumeric("Blues", reverse = TRUE, domain = cvilleshapestr$CANCER)
leaflet(cvilleshapestr) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvilleshapestr,
fillColor = ~pal(CANCER),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", cvilleshapestr$GEOID, "<br>",
"Cancer Risk: ", cvilleshapestr$T_CANCR)) %>%
addLegend("bottomright", pal = pal, values = cvilleshapestr$CANCER,
title = "Air Toxics Cancer Risk", opacity = 0.7)
pal <- colorNumeric("plasma", reverse = TRUE, domain = cvilleshapestr$DSLPM)
leaflet(cvilleshapestr) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvilleshapestr,
fillColor = ~pal(DSLPM),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", cvilleshapestr$GEOID, "<br>",
"DSLPM: ", cvilleshapestr$T_DSLPM)) %>%
addLegend("bottomright", pal = pal, values = cvilleshapestr$DSLPM,
title = "Diesel Particulate Matter Level", opacity = 0.7)
pal <- colorNumeric("Blues", reverse = TRUE, domain = cvilleshapestr$OZONE)
leaflet(cvilleshapestr) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvilleshapestr,
fillColor = ~pal(OZONE),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", cvilleshapestr$GEOID, "<br>",
"Ozone Level: ", cvilleshapestr$T_OZONE)) %>%
addLegend("bottomright", pal = pal, values = cvilleshapestr$OZONE,
title = "Ozone Levels in the Air", opacity = 0.7)
These scatterplots show the relationship between pairs of EJSCREEN measures.
ejscreen %>%
ggplot() +
geom_point(aes(x=OZONE, y=PM25, color=countyfips)) +
labs(x="Ozone level",
y="PM2.5 level") +
scale_color_brewer(type = "qual", labels = c("Albemarle", "Fluvanna", "Greene", "Louisa", "Nelson", "Charlottesville"))
ejscreen %>%
ggplot() +
geom_point(aes(x=PTRAF, y=CANCER, color=countyfips)) +
labs(x="Proximity to traffic",
y="Cancer risk") +
scale_color_brewer(type = "qual", labels = c("Albemarle", "Fluvanna", "Greene", "Louisa", "Nelson", "Charlottesville"))
ejscreen %>%
ggplot() +
geom_point(aes(x=PTRAF, y=DSLPM, color=countyfips)) +
labs(x="Proximity to traffic",
y="Diesel particulate matter level") +
scale_color_brewer(type = "qual", labels = c("Albemarle", "Fluvanna", "Greene", "Louisa", "Nelson", "Charlottesville"))
ejscreen %>%
ggplot() +
geom_point(aes(x=PM25, y=DSLPM, color=countyfips)) +
labs(x="PM2.5 level",
y="Diesel particulate matter level") +
scale_color_brewer(type = "qual", labels = c("Albemarle", "Fluvanna", "Greene", "Louisa", "Nelson", "Charlottesville"))
PM2.5, ozone, and NATA indicators (cancer risk, respiratory hazard index, and diesel particulate matter) are measured at the census tract level, and the same value is assigned to each block group within that tract. All other variables were derived for the block group level.